A non-parametric test doesn't rely on any statistical distribution; that is why it is known as a "distribution-free" hypothesis test. Non-parametric tests don't have parameters of the population. Such types of tests are used for order and rank of observations and require special ranking and counting methods. Here are some examples of non-parametric tests:
- A Chi-Square test is determined by a significant difference or relationship between two categorical variables from a single population. In general, this test assesses whether distributions of categorical variables differ from each other. It is also known as a Chi-Square goodness of fit test or a Chi-Square test for independence. A small value of the Chi-Square statistic means observed data fit with expected data, and a larger value of the Chi-Square statistic means observed data doesn't fit with expected data. For example, the impact of gender on voting preference or the impact...
